Robust multi-target regression with improved stochastic configuration networks and its applications

• Published in: Information sciences 2025-01, Vol.689, p.121480, Article 121480
• Main Authors: Yan, Aijun, Hu, Kaicheng, Wang, Dianhui, Tang, Jian
• Format: Article
• Language: English
• Subjects: Hidden layer parallel construction; Matrix elastic net; Multi-target regression; Robust modeling; Stochastic configuration networks
• ISSN: 0020-0255
• DOI: 10.1016/j.ins.2024.121480

To improve the accuracy and robustness of stochastic configuration networks (SCNs) for resolving multi-target regression tasks, this paper proposes a robust modeling approach based on improved stochastic configuration networks. A parallel implementation of SCN models is designed to incrementally generate the hidden nodes, which enhances the diversity of hidden layer mapping through information superposition and spanning connection. We employ an elastic net regularization model to sparsely constrain the model parameters to characterize the correlation among multiple targets. Then, the mixture Laplace distributions are used as the prior distribution of each target modeling error, and the output weights of the SCN model are re-evaluated by maximizing a posteriori estimation to enhance model’s robustness with respect to some uncertainties presented in training samples. The modelling performance of the proposed solution is tested on six standard datasets and the historical data of a municipal solid waste incineration process. The experimental results show that the proposed modeling technique has advantages in terms of both the prediction accuracy and the robustness.